Awesome
MICN
Code release of paper "MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting" (ICLR 2023 oral)
MICN achieve SOTA on six benchmarks.
Overall Architecture
As shown in Figure 1, we decompose the time series into seasonal part and trend part by Multi-scale Hybrid Decomposition. For seasonal part, we use Seasonal Prediction block to predict. For trend part, we use simple regression to predict.
<p align="center"> <img src=".\img\overall.png" height = "202" alt="" align=center /> <br><br> </p>Seasonal Prediction block
The seasonal part contains several different patterns after Multi-scale Hybrid Decomposition. For each pattern, we use local-global module to extract local information and global correlations.
<p align="center"> <img src=".\img\seasonal.png" height = "250" alt="" align=center /> <br><br> </p>Local-Global module
We use downsampling convolution to extract local features and isometric convolution to capture global correlations.
<p align="center"> <img src=".\img\local-global.png" height = "150" alt="" align=center /> <br><br> </p>Get Started
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pip install -r requirements.txt -
Data. All the six benchmark datasets can be obtained from Google Drive or Tsinghua Cloud.
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Reproducibility. We provide the experiment scripts of all benchmarks under the folder
./scripts. You can reproduce the experiments results by:
bash ./scipts/ETTm.sh
bash ./scipts/ETTh.sh
bash ./scipts/ECL.sh
bash ./scipts/Exchange.sh
bash ./scipts/Traffic.sh
bash ./scipts/WTH.sh
bash ./scipts/ILI.sh
Experiments
Main Results
Multivariate results
Univariate results
Model Analysis
Local-global vs. self-attetion, Auto-correlation
Visualization
Visualization of learned trend-cyclical part prediction and seasonal part prediction.
Contact
If you have any questions, please contact wanghuiqiang@stu.scu.edu.cn. Welcome to discuss together.
Citation
If you find this repo useful, please cite our paper
@article{micn,
title={MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting},
author={Huiqiang Wang and Jian Peng and Feihu Huang and Jince Wang and Junhui Chen and Yifei Xiao},
booktitle={International Conference on Learning Representations},
year={2023}
}
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/thuml/Autoformer
https://github.com/zhouhaoyi/Informer2020